{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Quick, Draw!* GAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we use Generative Adversarial Network code (adapted from [Rowel Atienza's](https://github.com/roatienza/Deep-Learning-Experiments/blob/master/Experiments/Tensorflow/GAN/dcgan_mnist.py) under [MIT License](https://github.com/roatienza/Deep-Learning-Experiments/blob/master/LICENSE)) to create sketches in the style of humans who have played the [*Quick, Draw!* game](https://quickdraw.withgoogle.com) (data available [here](https://github.com/googlecreativelab/quickdraw-dataset) under [Creative Commons Attribution 4.0 license](https://creativecommons.org/licenses/by/4.0/))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/the-deep-learners/deep-learning-illustrated/blob/master/notebooks/generative_adversarial_network.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_In order to efficiently carry out the training in this notebook, we recommmend using a GPU. Most readers don't have a GPU suitable for TensorFlow operations (i.e., an Nvidia GPU with CUDA and cuDNN drivers installed) available on their local machine, however you can easily access one for free via [Colab](https://colab.research.google.com/)._ \n",
    "\n",
    "_Separately, for reasons that escape us, the discriminator in this notebook nearly always fails to learn if you train on a CPU only. Because of this failure, the GAN will seldom learn how to generate sketches -- i.e., it will output images that are merely random noise. There are two ways we've identified to remedy this situation:_ \n",
    "\n",
    "1. _**Use a GPU.** If you don't have one, use Colab as suggested above. While in Colab, you can select \"Change runtime type\" from the \"Runtime\" item in the menu bar, and choose \"GPU\" as your hardware accelerator. This hardware accelerator trains the GAN orders of magnitude more rapidly than the \"None\" or \"TPU\" options, and the discriminator (we have no idea why!) will train properly._\n",
    "2. _**Change the discriminator's optimizer.** As noted by a comment in this notebook's discriminator compilation step, switching from the default RMSprop optimizer to another (e.g., Adam or AdaDelta) enables the discriminator to learn effectively and therefore the GAN generates sketches. Whether you use a CPU only, a GPU, or a TPU, this solution is effective. (That said, training the GAN with a GPU is still way faster than with a CPU only or a TPU.)_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# for data input and output:\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# for deep learning: \n",
    "import keras\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense, Conv2D, Dropout\n",
    "from keras.layers import BatchNormalization, Flatten\n",
    "from keras.layers import Activation\n",
    "from keras.layers import Reshape # new! \n",
    "from keras.layers import Conv2DTranspose, UpSampling2D # new! \n",
    "from keras.optimizers import RMSprop # new! \n",
    "\n",
    "# for plotting: \n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data\n",
    "NumPy bitmap files are [here](https://console.cloud.google.com/storage/browser/quickdraw_dataset/full/numpy_bitmap) -- pick your own drawing category -- you don't have to pick *apples* :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_If you're using Colab, the easiest way to load the data is to access it from Google Drive (detailed instructions are provided [here](https://colab.research.google.com/notebooks/io.ipynb#scrollTo=u22w3BFiOveA))._\n",
    "\n",
    "*For example, after we downloaded the \"apple.npy\" file, we placed it in our personal Google Drive in a subdirectory called \"quickdraw_data\" within a directory called \"Colab Notebooks\" (Colab creates this directory for saving notebooks by default). [Here's a screenshot](https://github.com/the-deep-learners/deep-learning-illustrated/blob/master/img/apples-in-drive.png) of what that looks like in the Google Drive web-browser interface. The data in place, we then ran the following (commented out) commands before executing the remainder of the notebook:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/gdrive')\n",
    "# os.chdir('/content/gdrive/My Drive/Colab Notebooks/quickdraw_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_images = \"../quickdraw_data/apple.npy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.load(input_images) # 28x28 (sound familiar?) grayscale bitmap in numpy .npy format; images are centered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144722, 784)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[4242]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144722, 28, 28, 1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data/255\n",
    "data = np.reshape(data,(data.shape[0],28,28,1)) # fourth dimension is color\n",
    "img_w,img_h = data.shape[1:3]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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     "data": {
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sS1LJ3XOfEzazqyXtk7Tc3S8qb3tC0m53f7z8D+eZ7v5gQXp7WNK+vFduLi8oM67vytKS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1777a1a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(data[4242,:,:,0], cmap='Greys')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create discriminator network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_discriminator(depth=64, p=0.4):\n",
    "\n",
    "    # Define inputs\n",
    "    image = Input((img_w,img_h,1))\n",
    "    \n",
    "    # Convolutional layers\n",
    "    conv1 = Conv2D(depth*1, 5, strides=2, \n",
    "                   padding='same', activation='relu')(image)\n",
    "    conv1 = Dropout(p)(conv1)\n",
    "    \n",
    "    conv2 = Conv2D(depth*2, 5, strides=2, \n",
    "                   padding='same', activation='relu')(conv1)\n",
    "    conv2 = Dropout(p)(conv2)\n",
    "    \n",
    "    conv3 = Conv2D(depth*4, 5, strides=2, \n",
    "                   padding='same', activation='relu')(conv2)\n",
    "    conv3 = Dropout(p)(conv3)\n",
    "    \n",
    "    conv4 = Conv2D(depth*8, 5, strides=1, \n",
    "                   padding='same', activation='relu')(conv3)\n",
    "    conv4 = Flatten()(Dropout(p)(conv4))\n",
    "    \n",
    "    # Output layer\n",
    "    prediction = Dense(1, activation='sigmoid')(conv4)\n",
    "    \n",
    "    # Model definition\n",
    "    model = Model(inputs=image, outputs=prediction)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "discriminator = build_discriminator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 14, 14, 64)        1664      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 7, 7, 128)         204928    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 4, 4, 256)         819456    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 4, 4, 512)         3277312   \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 8193      \n",
      "=================================================================\n",
      "Total params: 4,311,553\n",
      "Trainable params: 4,311,553\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "discriminator.compile(loss='binary_crossentropy', \n",
    "                      optimizer=RMSprop(lr=0.0008, \n",
    "                                        decay=6e-8, \n",
    "                                        clipvalue=1.0), \n",
    "                      metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_N.B.: Bizarrely, if you're using a CPU only to train your model (i.e., you're not using a GPU), then the RMSprop optimizer nearly always fails to train the discriminator. Instead of compiling the discriminator with RMSprop you could compile it with AdaGrad or Adam, as in the following (commented out) cell block._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from keras.optimizers import Adam \n",
    "# discriminator.compile(loss='binary_crossentropy', \n",
    "#                       optimizer=Adam(lr=0.0008, \n",
    "#                                      clipvalue=1.0), \n",
    "#                       metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create generator network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z_dimensions = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_generator(latent_dim=z_dimensions, \n",
    "                    depth=64, p=0.4):\n",
    "    \n",
    "    # Define inputs\n",
    "    noise = Input((latent_dim,))\n",
    "    \n",
    "    # First dense layer\n",
    "    dense1 = Dense(7*7*depth)(noise)\n",
    "    dense1 = BatchNormalization(momentum=0.9)(dense1) # default momentum for moving average is 0.99\n",
    "    dense1 = Activation(activation='relu')(dense1)\n",
    "    dense1 = Reshape((7,7,depth))(dense1)\n",
    "    dense1 = Dropout(p)(dense1)\n",
    "    \n",
    "    # De-Convolutional layers\n",
    "    conv1 = UpSampling2D()(dense1)\n",
    "    conv1 = Conv2DTranspose(int(depth/2), \n",
    "                            kernel_size=5, padding='same', \n",
    "                            activation=None,)(conv1)\n",
    "    conv1 = BatchNormalization(momentum=0.9)(conv1)\n",
    "    conv1 = Activation(activation='relu')(conv1)\n",
    "    \n",
    "    conv2 = UpSampling2D()(conv1)\n",
    "    conv2 = Conv2DTranspose(int(depth/4), \n",
    "                            kernel_size=5, padding='same', \n",
    "                            activation=None,)(conv2)\n",
    "    conv2 = BatchNormalization(momentum=0.9)(conv2)\n",
    "    conv2 = Activation(activation='relu')(conv2)\n",
    "    \n",
    "    conv3 = Conv2DTranspose(int(depth/8), \n",
    "                            kernel_size=5, padding='same', \n",
    "                            activation=None,)(conv2)\n",
    "    conv3 = BatchNormalization(momentum=0.9)(conv3)\n",
    "    conv3 = Activation(activation='relu')(conv3)\n",
    "\n",
    "    # Output layer\n",
    "    image = Conv2D(1, kernel_size=5, padding='same', \n",
    "                   activation='sigmoid')(conv3)\n",
    "\n",
    "    # Model definition    \n",
    "    model = Model(inputs=noise, outputs=image)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "generator = build_generator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3136)              103488    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 3136)              12544     \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 3136)              0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 14, 14, 32)        51232     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 14, 14, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 28, 28, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTr (None, 28, 28, 16)        12816     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 28, 28, 16)        64        \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 28, 28, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTr (None, 28, 28, 8)         3208      \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 28, 28, 8)         32        \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 28, 28, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 28, 28, 1)         201       \n",
      "=================================================================\n",
      "Total params: 183,713\n",
      "Trainable params: 177,329\n",
      "Non-trainable params: 6,384\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "generator.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create adversarial network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z = Input(shape=(z_dimensions,))\n",
    "img = generator(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "discriminator.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = discriminator(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adversarial_model = Model(z, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adversarial_model.compile(loss='binary_crossentropy', \n",
    "                          optimizer=RMSprop(lr=0.0004, \n",
    "                                            decay=3e-8, \n",
    "                                            clipvalue=1.0), \n",
    "                          metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(epochs=2000, batch=128, z_dim=z_dimensions):\n",
    "    \n",
    "    d_metrics = []\n",
    "    a_metrics = []\n",
    "    \n",
    "    running_d_loss = 0\n",
    "    running_d_acc = 0\n",
    "    running_a_loss = 0\n",
    "    running_a_acc = 0\n",
    "    \n",
    "    for i in range(epochs):\n",
    "        \n",
    "        # sample real images: \n",
    "        real_imgs = np.reshape(\n",
    "            data[np.random.choice(data.shape[0],\n",
    "                                  batch,\n",
    "                                  replace=False)],\n",
    "            (batch,28,28,1))\n",
    "        \n",
    "        # generate fake images: \n",
    "        fake_imgs = generator.predict(\n",
    "            np.random.uniform(-1.0, 1.0, \n",
    "                              size=[batch, z_dim]))\n",
    "        \n",
    "        # concatenate images as discriminator inputs:\n",
    "        x = np.concatenate((real_imgs,fake_imgs))\n",
    "        \n",
    "        # assign y labels for discriminator: \n",
    "        y = np.ones([2*batch,1])\n",
    "        y[batch:,:] = 0\n",
    "        \n",
    "        # train discriminator: \n",
    "        d_metrics.append(\n",
    "            discriminator.train_on_batch(x,y)\n",
    "        )\n",
    "        running_d_loss += d_metrics[-1][0]\n",
    "        running_d_acc += d_metrics[-1][1]\n",
    "        \n",
    "        # adversarial net's noise input and \"real\" y: \n",
    "        noise = np.random.uniform(-1.0, 1.0, \n",
    "                                  size=[batch, z_dim])\n",
    "        y = np.ones([batch,1])\n",
    "        \n",
    "        # train adversarial net: \n",
    "        a_metrics.append(\n",
    "            adversarial_model.train_on_batch(noise,y)\n",
    "        ) \n",
    "        running_a_loss += a_metrics[-1][0]\n",
    "        running_a_acc += a_metrics[-1][1]\n",
    "        \n",
    "        # periodically print progress & fake images: \n",
    "        if (i+1)%100 == 0:\n",
    "\n",
    "            print('Epoch #{}'.format(i))\n",
    "            log_mesg = \"%d: [D loss: %f, acc: %f]\" % \\\n",
    "            (i, running_d_loss/i, running_d_acc/i)\n",
    "            log_mesg = \"%s  [A loss: %f, acc: %f]\" % \\\n",
    "            (log_mesg, running_a_loss/i, running_a_acc/i)\n",
    "            print(log_mesg)\n",
    "\n",
    "            noise = np.random.uniform(-1.0, 1.0, \n",
    "                                      size=[16, z_dim])\n",
    "            gen_imgs = generator.predict(noise)\n",
    "\n",
    "            plt.figure(figsize=(5,5))\n",
    "\n",
    "            for k in range(gen_imgs.shape[0]):\n",
    "                plt.subplot(4, 4, k+1)\n",
    "                plt.imshow(gen_imgs[k, :, :, 0], \n",
    "                           cmap='gray')\n",
    "                plt.axis('off')\n",
    "                \n",
    "            plt.tight_layout()\n",
    "            plt.show()\n",
    "    \n",
    "    return a_metrics, d_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_N.B.: Running the next cell leads to the following warning in recent versions of Keras:_\n",
    "\n",
    "Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
    "\n",
    "_This warning can be safely ignored. In most neural networks, you should indeed ensure that you compile the model after adjusting whether the models weights are trainable or not. In a GAN, however, this is unnecessary: The discriminator need not be compiled after making its weights untrainable; instead, we compile these untrainable weights as a part of the adversarial model. You can read more about this unnecessary warning [here](https://github.com/keras-team/keras/issues/8585) and [here](https://github.com/tensorflow/tensorflow/issues/22012). If the warning **really** bothers you though, we created a GAN notebook with an awkward workaround [here](https://github.com/the-deep-learners/deep-learning-illustrated/blob/master/notebooks/awkward-GAN-with-no-warning.ipynb)._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #99\n",
      "99: [D loss: 0.308268, acc: 0.938605]  [A loss: 3.351842, acc: 0.289694]\n"
     ]
    },
    {
     "data": {
      "image/png": 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995ykxChybMxjOzlmhNXBGmH5Ojs7CxaTY8Yet/Tf4Nrc4nlMKWeFW42nnnpK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\n1bK9j3Mr8i6jnlbuvbtZ2YBFwX6QGfFC9Nr7E6O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QdbRdN9xXyn7CnWPIfNa0\nPA3unTYi87Td+Wt1fKt95C6Xy9e95vp3We0kvJLzJHOo05KZdWYfmRfsPh+9cq532u67tk+9Jr71\nNujKlFVtfSRb7L3uJuUOt6trIud2xjfj2hlyrxLt2vfr9Xp6re/uehVvPqvruPt9GefcDfX/7xsZ\nMcCwP50jBuBMRgwwTCAGGCYQAwwTiAGGCcQAwwRigGECMcAwgRhgmEAMMEwgBhgmEAMME4gBhgnE\nAMMEYoBhAjHAMIEYYJhADDBMIAYYJhADDBOIAYYJxADDBGKAYQIxwLAfm5LzA251ie0AAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd108579f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #199\n",
      "199: [D loss: 0.294289, acc: 0.928765]  [A loss: 3.554233, acc: 0.174545]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ya621gJIh2RbbLoh4jnEmoo/e36QzD9ujinijjTYC5tvM9/i7OOqoo4CRM0r/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\n2/OdbVNtUeG47XdVJlZ8rk31EY/ndVl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FiOvXj3zvvfcC8K9//QsolRydGV5yySVA6Uec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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1081c9668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #299\n",
      "299: [D loss: 0.333603, acc: 0.898411]  [A loss: 3.123141, acc: 0.154473]\n"
     ]
    },
    {
     "data": {
      "image/png": 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IDVXEU001VZmP2NFG3/A000wDpAwtV34d5fWpGWWx3XbbAXDBBRcAE86KyWOY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\nwgorAMk/ra99//33B8bfi/b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ByW+38sorAylKQl+wUQLmsTuK1muXopVFV1WFaC8VwMiR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\nF4Xhfm6VdPPNN/fU5UyQUMRBEAQFU6girlSYxxG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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0ec20d400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #399\n",
      "399: [D loss: 0.368001, acc: 0.870262]  [A loss: 2.777383, acc: 0.156505]\n"
     ]
    },
    {
     "data": {
      "image/png": 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KO+/SpQsQ7H733XeX7VpkV13TZpttVpTjWhEbY0xkUhEjVgOdfffdF4C77roL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\n50xPXP3Mlnut/G5VL+k+SsHkm7OZdts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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd108045fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #499\n",
      "499: [D loss: 0.390248, acc: 0.849731]  [A loss: 2.572680, acc: 0.156688]\n"
     ]
    },
    {
     "data": {
      "image/png": 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WVrFlxWOquPWll14KhKaLlYiNVMtztbtRnVL5UlVDtxpVyqqB/GjaVT/00EOB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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0bc5eeef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #599\n",
      "599: [D loss: 0.404920, acc: 0.837522]  [A loss: 2.442488, acc: 0.155559]\n"
     ]
    },
    {
     "data": {
      "image/png": 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shE033RQIykFPzdNOOw0IW7/n61OKrSzyVRXq56F8yF69egEwcuRIoG5PfuWsqiJSFXSq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\n90T/ry52UrcQbKff6VW+/CQ6h9TIhx9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0ec580240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #699\n",
      "699: [D loss: 0.410297, acc: 0.831282]  [A loss: 2.377832, acc: 0.153791]\n"
     ]
    },
    {
     "data": {
      "image/png": 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LdeEoNqrDQnXM/MCBA/O5pAWIrSyqs60m2enUCGXZd911VyDMgygE8jZU56kMv+ZKSxFn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\n6q3+puQ+xAwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0bc571128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #799\n",
      "799: [D loss: 0.412047, acc: 0.827812]  [A loss: 2.342445, acc: 0.152476]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0c43e7780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #899\n",
      "899: [D loss: 0.412766, acc: 0.825488]  [A loss: 2.326389, acc: 0.150697]\n"
     ]
    },
    {
     "data": {
      "image/png": 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EqFGjgJDpEyOLQrnN8q2r2jLAitgYYyJT0opYHpAUmH7Kx33rrbcK9l5S3+nN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\n8nflCYts37WmTZum/EypaPUAUW8EXS9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd1080d0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #999\n",
      "999: [D loss: 0.412487, acc: 0.824074]  [A loss: 2.315010, acc: 0.149032]\n"
     ]
    },
    {
     "data": {
      "image/png": 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48sorgaDqcnHveuIpdzp48GAgHMo4adKkjP5ObGVRVWzlg9bhh6rnV/5dR5ar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\nbIwxkfFEbIwxkfFEbIwxkfk/kcsXNPKdjo4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0ec5c7908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1099\n",
      "1099: [D loss: 0.410315, acc: 0.824109]  [A loss: 2.323983, acc: 0.146461]\n"
     ]
    },
    {
     "data": {
      "image/png": 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htj6fPQikynUNUnODBg0CktH/IhuowlNd5YYNGwbAJptsAmRHXWkg5tixY4Gg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\nbIwxkfFGbIwxkfl/v/Jjt+SNV5oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0c43e8550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1199\n",
      "1199: [D loss: 0.407442, acc: 0.824668]  [A loss: 2.340701, acc: 0.144691]\n"
     ]
    },
    {
     "data": {
      "image/png": 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5sLXCnDFjRrVfzz9hRWyMMZFJlCIeNWoUEJpdKyckv2Ax0M749ttvX+65i1nd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\n5mnS0CbWNddcA4RmKs8++yxQ9aPfk4J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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0ec1989e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1299\n",
      "1299: [D loss: 0.404047, acc: 0.825867]  [A loss: 2.361092, acc: 0.142622]\n"
     ]
    },
    {
     "data": {
      "image/png": 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QYqs1jQEVCupLe9111wFwzDHHAMHWUiNt27YFapZnLOWr3gGqOIsx2iYfqHLw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\n9dVXgZB2onZ4apSueJ9SZ4q9ybnU5y6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0ec375080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1399\n",
      "1399: [D loss: 0.400849, acc: 0.827095]  [A loss: 2.380299, acc: 0.140815]\n"
     ]
    },
    {
     "data": {
      "image/png": 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oY0plKOS6OWI+5Lq1ezbKQ1Y8rhjXVkzUIlSKWD0OdtxxRyCuF9W8eXMgbDaq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\nj3Kt04TSHnN9wD3wwANAWOR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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0ec249710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1499\n",
      "1499: [D loss: 0.397709, acc: 0.828391]  [A loss: 2.400541, acc: 0.138613]\n"
     ]
    },
    {
     "data": {
      "image/png": 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lc3zjjTeA4CNWJLlQJMW26keiwbRqCK9YRz6+a1KDL7zwAgDnnXceEIbeZkts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\n9pqFQM2ppDIzTYVSOXl6E/5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0ec783898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1599\n",
      "1599: [D loss: 0.394321, acc: 0.829828]  [A loss: 2.426607, acc: 0.136487]\n"
     ]
    },
    {
     "data": {
      "image/png": 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9vcuxlUW+qkIohqrIOuecc4CQS9eOBOpd8fbbb2d8vpqpSPHqc9AKs2Ybcseo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\nWmcoxHWr74a+K2pmo3xqrjnM2LGFmuO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0ec24bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1699\n",
      "1699: [D loss: 0.390676, acc: 0.831433]  [A loss: 2.448597, acc: 0.134541]\n"
     ]
    },
    {
     "data": {
      "image/png": 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VsTHGlAmJUsRCuUTlGlXRpH6kd955Z5XfZ8pZ5oK6q33xxRdA6HOh3FBNKie2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\nhCOR5C/MtqlKx44dU1V52ebCtEMrNVO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0ec723438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1799\n",
      "1799: [D loss: 0.387178, acc: 0.832885]  [A loss: 2.474159, acc: 0.132565]\n"
     ]
    },
    {
     "data": {
      "image/png": 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sP9/YtoXq7au4rN6TFLC6XnU8Ue/evYFQOy21qrGq8rD322+/VNxY3ZCK8bZq\n1QoI3xdVTakuX4e5asi/4s56bR2Lpa5K/btjxMYYkxDKIkYsFIdRbFi1r+p60TSlmsRvFSuVEtah\ni/kq4XJFMSsdOa4DFhWXLYVqS0cqRJ+n6NixI5Cc2RJCNlQ8UUf3FNO28hrVEabDcHfccUegPKti\n8kXeuo5A0gG+ml+uucQ6/EBxWXXoaf/o2bNn6jVlHw2NV5+AjpqSElZ8WvkLKWl11qrnQYPmaxpZ\nsCI2xpjIlFWMOB3FhhTHST/BQxnNn376CVj4U0n1wTo+WzWgqq9V3V++xI61VWdbxV+lKlQvqVhX\nMbvYFKfWYY6nnnoqEGwtJZxphmu521Yzqrt37w4ElVXIKhApX61fnQai6phBgwbl9bqxbQu57wvz\n/B4Q4ruKIeu7vc8++wAwefLkan83vT5Y3w+tXXmW+qxznbLmGLExxiSEslbEQhnLW2+9FQixOMWU\n1VU0duxYoFIp6/8p1qNTE/T07NGjR5XfrSmxlUUm22600UZAqHVV55165z/77DMgPPGlBFRfPe9Z\naKrBVs2kalhVDaH4u2L6+ncpk+233x4IXX+ZKHfbytvSfAdV+yiLf/fddwMwYsQIIOQj1JE3r23l\n9WnerTo/BwwYAIT1rO49dfPl8F6A8ulahPz3hYW8HlAe9ehWxMYYkxASoYiF1IJUrXrPjz/+eADa\ntm0LVCoLxXDU+aK6PnXtnHfeeTW5lfmIrSyyta1qsjVDI/1kB6mJdOZdJ+k/o3+TjaUE1cc/ZMgQ\nAMaPHz/fa2VDUmwru2yzzTYAqdMglHlX7DL9HMEF2UPZfXl9EydOBMJp4rXFk4PCK+JyworYGGMS\nQqIU8UJeFwixuhYtWtCyZUsgdOG89957QIhTFvp9x1YWudpWWWDZR96E/q74r2yrWPGsWbNStlP1\nw9dffw2E2kzNRC7UaSZJs206qk1VrLxLly5A8Oxkx99//z3l9alKQjWyxTqLMLZtwYoYrIiNMSY6\ntUIRlwOxlYVtWzxs2+Ji+1oRG2NMdEqqiI0xxsyPFbExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7Ex\nxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTG\nG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7Ex\nxkTGG7Ga2pWGAAAfU0lEQVQxxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTGG7ExxkTG\nG7ExxkSmTikvVlFRMbeU1yslc+fOrYh5fdu2eNi2xcX2LfFGXGgWWaRS0G+88cYALLHEEgCMGjWK\nf/75J9p9/VfR59GtWzcAevbsCUC/fv0A+OGHH+LcWEKoV69eag3/+OOPke8meSy22GIArL322gBM\nmjQJgL///jvaPWWLQxPGGBOZRCvi/fbbD4CBAwcCMHdupYczePBgLrnkkmj39V9DSnjPPfcE4Pbb\nbwegTp3K5TVs2DAAHnzwwQh3V/7Ifuecc07qv0899dSYt5RINtlkEwAeeeQRAG655RYA+vfvH+2e\nssWK2BhjIpNIRbzssssCcMMNNwDwyy+/ADB06FAANt1009TPOC5ZfI444ggALrvsMgA+++wzALp3\n7w7A1KlTo9xXuVNRUZnH2XnnnQE46aSTeO6552LeUqL55JNPAFhyySUBOOaYY4CwHocMGRLlvrLB\nitgYYyKTSEW81lprAdCgQQMA6tatCwSF0ahRI3r06AGEuOTPP/9c6tus9SjDf9VVVwHB/uuvvz4A\nf/75Z5wbSwjLL788UJnTgEr7Kc5ucmfrrbcGQm5izpw5AOy///4ATJw4kbfeeguAWbNmRbjD6rEi\nNsaYyFSo0qAkFytQ4XbDhg2BEBvW023llVcGYPbs2ak4kZSx4pfFInZhfIyi+HvvvReAfffdt8rf\npUAKRW2z7eKLLw6ErL7sB6EWtlTEti3U3L76risW3KRJEwD++OMPgFRPwbRp01L/T3tG27ZtARg5\nciQQat5/+umnmtxSimzta0VsjDGRSWSMOB3VXjZt2hSAjz/+ONWZNHz48Gj3VVtZbrnlANhoo42A\nSnsDXHnllVV+TjHjUnpdSUAK7ZprrgFg2223BYKSM9kh76Fv375AiA3PmDEDgDfeeAMIOYvvv/+e\niy66CICxY8cCoSt3wIABADz++OMAvP3220BlbTcUP8dkRWyMMZFJZIxYT0LFe/766y8A7r//fgDu\nvPNOpk+fDoSnoeKXxXq/sWNt+dp2qaWWAuCAAw4AYNFFFwXgoYceAkjZURnoRRZZhMceewwIPf1S\nHqqBXWaZZQDYaqutgBCff+mll4DweWVLUm1bHfLgOnXqBMDzzz8PVFb7KH5cqlkpsW0L+dv31ltv\nBeZfu8odicaNGwPw1FNPpWq2q9sHOnToAISYsdS1KrVyxTFiY4xJCImKEUtJbLrppgv8986dOwOV\nWWnFi7bYYgsAdthhByDMQRg/fjwQYj//lTjmmmuuCcB9990HwIorrljl32U31QYre7z55psD0LVr\n11THnDLPUr5SG1Im+lOfgeL2iunpHmob8jLU3TlvjBKCdyFv49FHHwUqqydUGy9bSSG3bNkSCEpZ\nSk2Txf4r63depFLVuamqCeWFBg0aVOXnx40bl9FOkydPBkK1lbw8TRSU91JorIiNMSYyZR0jljpT\nt5EmqjVr1gwIakEKQ2rh008/5aOPPgKgffv2QMj016tXr8o1Pv/8cwAefvjhKtdIjzNlInasLZNt\nV111VSDEaWfPng3AaaedBlTOcAb47bffANh7770BOP3004Gg8ho0aJBSuu+++y4Aq6++OgD//vsv\nAJttthkAU6ZMAaB169YAXH755QCss846APTu3RuojN0tjHK3rVB9+4QJEwBo06YNEDy59CoSKTl5\nZRUVFdx9990AHH744QCsttpqVa4hBfz7778D8OGHHwIhP6LYvtbvzJkzq1wzndi2hdz3hZdffhkI\n09a++OILAFq0aAGE3JE+D9m9adOmWVc/6Hc++OADIHgkzZs3B4JdM+EYsTHGJISyVMSKrf3f//0f\nEGKMUry//vorEJSEVMH1118PwOjRo1OKT082qWfFlXbZZRcAdtxxRyB02MgeN910ExAUoV6vOmIr\ni+psq/ev2JYUlmqApSYW8rpAsHH79u1TE+1UaykFrM/p9ddfX+hr3nXXXUD4DKQcq+tmKlfbCile\nve811lgDgOOOOw4gVWWi9bvXXnsBcPLJJwNhzf3555+p7lDNTxk3bhwQJg0qhqyKi9133x0IHos8\nG9W/vvnmmwt9b7FtC9nvC7LrO++8A4Rpi5pHrveavqe9+uqrQMhz5IJyKFLG8jhUqZEJK2JjjEkI\nZaWIpQLef/99IMRzjz/+eACefPJJYP4aS70HxYpzQfFOxZ0Vx1S3k2Kqe+yxx0KvEVtZVGdbeQJS\nvqqnPvHEE3N6fc1DuOeee1IxuDPOOAOorNuG7OPq+lxVNaD4/MEHH7zAny9X24quXbsC8PTTTwOh\nKiTT/FvZQWtM1RUQ1pvUdHWoRlYezxNPPAHA+eefD2T+TsS2LWS2r7wyecCaLSHvQHXpylloKqC+\n2/ICtW7z4cUXXwTCZ6Qu3kxT3KyIjTEmIZRFHbGeeHrqKNvZsWNHoHJqUrFQ7Pebb74BwuSws846\nCwjTmBST0zzTpCBbSkU888wzeb2OsvIDBgxIKQ51NuWqNOTRfP3110D4nJPKhRdeCIR1qvxCJqSm\nNDN7ww03TNkmUyWJUPWElLFqafPxDssV5XXq168PhO9geuWC+gv0HT7ssMMW+HP5oEl5yoeoCknx\n6ppiRWyMMZEpixixMvijR48GwhSvmCfZKr6krLVUoDrTVDMrYsfaqrOtYt9SSjrFYMyYMXldZ8MN\nN2SnnXYC4OyzzwZy7+qSB6SuJeUGqstql6ttFeNVl9t1110H5L5uVfEwbdq0lOeiulXF0asjvZJA\nJxirDjwTsW0LmWPEyg2pKkdrOpPSVR9C+nc1H6TGVbGlrj19B6ojW/uWRWhCYQAZTImGmChkoeSd\nyofkkrz33ntxbixP1AigB0q+vPHGG6khP/mih5w2kXwfCrFp1KgREDZkNRHlisILSy+9dKp8LVt3\nWhuDWqIlZmoDemBrdIGajbK1TSE2YKFrKgx30EEHAaFMsKaC1qEJY4yJTFRFLGW03nrrAWHghhRC\nOaCmBRXtKymSFKQipA7UuBITqXKNy8zUAFKuKNlWyPCejoTPFh3Qqu+MwjtqbqoNKMSgpqKYaHB8\nz549gTBYXk0j+WJFbIwxkYmqiBVbU4Litddei3k7C0RxKg2jT1pZkFSbYtrHHHMMEMaBKnZcSjRQ\nXqhsMWnIy5ANFa8tJYoNa3jVuuuuW/J7KBbyNOSNqoxNnnSmsQPFQO3UanG+5557gHBwcb77gxWx\nMcZEJqoilsrUk03xrnJi6aWXBqo/hqXckapQS7MaOjRmUSVXpUSlVRryozh80pC3ISUsxabBMKVA\nn6/ae5VvkYpMmge3INRMdOihhwLhsFq1G6t8sBSo8UvNSBpQphI7HY6Qq1q3IjbGmMhEVcRSwPpT\nA8bLCakcxQPVGJE01BYqtXbmmWcCoe504sSJRb8HVUuoBVXDbgpZ71lKdN/KmKvRRS3PmQbCFBJ5\nF6owUG6jNnDSSScBYd0ot6Q6bsXJS9Gcps/8mmuuAcKoU7VXL7/88kDm8bLpWBEbY0xkoipiZZtV\nP6wjdBQ7LtWR4gtDg+OlOAoxQCQGUgsnnHACEFqdpUpVf6pYYzHo1asXEFp6NT4z6agz9IUXXgBC\nN2afPn2A0ig1eRtan7UhNiwUb5UdNchq1113BUKeQ+Mwi4E8jCZNmgDzV3CoXj/fyhkrYmOMiUxZ\nzJro378/AMOGDQNCH/fNN98c65ZSsTZloVXjnHSlIS9DGWcNi1GseJ999gHCQJ5CqrkLLrgACOph\nxIgRBXvtmOggA40F1YD7kSNHApmHu9cEVUeoEmXSpElAaVR4qVCs/Y477gBg++23B0K1RPohwrKJ\nPO58bCGl26pVKyCMwb344ouBcBixjg1TfX62h5OmY0VsjDGRKQtFPH78eCDU4umpoxGUMepMNfFJ\n8xCkbmoLimWpE0uHI2qMorwTeSWqqpgxY0bOXoGy3KrJ1jXKsW48H2QPxYqVOde8B82PKEZlSrt2\n7YBwlPxFF11U8GvERopW9txqq60A6NGjBxDWqpSz1KyUcjbKWKNYDzzwQCBUvmjN6ncVK/7uu+8A\n2GCDDYCa1zJbERtjTGTKYjC80MwJHcKop7xiQ1dffTVQXCWl2M+ECROA+Q8gVEwondgDtrM9krw6\n9OTXMT+Kwykbr3Xy5ZdfpoZiq+oh07yKo446CgjZbc0hlgrPRNJsq3Wsg1pXWWUVALbZZhug0oaF\n4r777gNCR5cGymebvY9tW8jevlKjqieW56wYvbpF1fWWroQrKipSuR/VIOuYLsX3NXReuRTNH1Yd\nvjzEoUOHApk76Hx4qDHGJISyUsRC8Zpzzz0XCIcAKg5z7bXXAuHocB3amE9Fg56QqvlUna3iS6rg\nyJThj60saqqI53kdIChhHRuuuuP+/fuz4oorAiH22aFDB2D+DjnVe6o/Xwol15nOSbWtlLFi4qqT\n33PPPYGanaahebiKmyo2LG8lW2LbFnK3r9aojk569NFHgaBONZPi+eefB6B58+ZAZUWQao9VDaEj\nkLQPPPzwwwAceeSRQM37BqyIjTEmIZSlIp7n5wFYaaWVALjtttuAygMs52XKlClAmA36wQcfpDLU\nquvT+5TS23LLLYHQx655oqrDVDwv26PiYyuLQiniLK6Tsq0OUlUsVCpC3Yjqx1f8Wd17uZ55l3Tb\nSnXpJBLFjHXag+zUtGnT1CkU6uRUrFKKTTXKp512GhDizaryyTV/Etu2UHP76vBh5ZbUYSf7StU2\nbtw4NRNCa1J7ijyKQk9XtCI2xpiEUNaKOJ30bpfzzjsPgC5dugAh46m61XnR+1QcSU9NzTYdMGAA\nELKhucabYyuLUiliCOpMnkjr1q2BYGN9TrKhajJVZ5srtcW2sssVV1wBQO/evYEQS66oqJgvCy+b\n6k+pO3XrHXvssUDoVsyV2LaFwtlXalf5HKnb7t27A5V13PKytTaLfcqHFbExxiSERCnihbwuEKot\nGjVqlOqIU+dR+/btgXDarbr2NGuhpk/G2MqilIpYqLJE8TXVZk6dOhUIMbqaTnSrrbbVFDp1NzZt\n2jQV41UuQz+jmPGYMWOAwp1KEdu2UHj7qjZYc4oLWbedK1bExhiTEGqFIi4HYisL27Z42LbFxfa1\nIjbGmOiUVBEbY4yZHytiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdi\nY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJ\njDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdi\nY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJjDdiY4yJTJ1SXqyiomJuKa9XSubOnVsR8/q2bfGw\nbYuL7WtFbIwx0SmpIjbJp27dusyZMweAf/75J/Ld1A4qKipFU8OGDbnyyisB+OSTTwAYNGgQQMrm\nJncWXXRRAJo0aUL9+vUB6NKlCwCvvPIKAFOnTo1zc/8fK2JjjIlMrVfE9erVA2CJJZYA4Pfffwfg\n77//jnZPSaJOncolsu222wJw//3389RTTwHQs2dPAGbPnh3n5moJiy22GAAjR45kvfXWA+CPP/4A\n4O233wZg2LBhcW6uFrDHHnsAcOONN/Lvv/8CsPTSSwNh7U6cOBGADTfcMMIdWhEbY0x0KubOLV3C\nspTZ0SZNmgBw+umnA/Dll18CcO211wKFV3Gxs8/Fsu2aa64JwJtvvglUxojlVXz99dcArLvuukBQ\ncYWmttpWSO3+73//Y8qUKQAcd9xxAAwcOBCADTbYAICmTZsC8Oeffxbk2rFtC8W379ChQwHYfvvt\nOfHEEwH48MMPgUqVDLDiiisCYU0vv/zyBbm2qyaMMSYh1CpFvMgilc+VfffdlyuuuAIIKm3VVVcF\nipfpj60sCm1bZfL/+usvIGSehw8fnvp/u+22GwA//fQTELyQQq+p2mZbsfXWWwPw9NNPA5UqV/Hi\nMWPGALDZZpsBIdfx2GOPAdCjR4+C3ENs20Lx9wV5D/Xq1WPChAkAXHjhhQB07twZgBNOOAGAGTNm\nANCyZcuCXNuK2BhjEkKtqpqQIr722mtTqq1du3aA6zBzRfWWUmhSua+//jpHH300EOLs/fr1q/I7\n6fF3/a4rVSpp1qwZAJdddhkAb7zxBlDptTVs2BCAzTffHAhVPvJQpOhUzaIqAFM9Dz74IFBZ5bP2\n2msDsMIKKwAwZMgQIHjKl156aYQ7tCI2xpjolJUilqLVnx06dABgm222AYI6Uwb/pZdeAsLTbMst\ntwSgcePGqVpXK+H8WHbZZYGguKRmW7RowUcffQTAscceC5DK9I8aNQoImX3FladNmwbAaaedBoTP\nrZT5iZhoPR922GEAHHrooUCoEb755psBuO2221Jx9r322gsI8WN5G4oVN2rUCIAffvih6PefdKZP\nnw6E9QjQsWNHAK6//noAzjrrLCDefmFFbIwxkSkLRSzFsPPOOwNw+OGHA7DOOusAsMwyywDzZ/Kf\nffZZAI488kiAVPxn7ty53HHHHVldW0pD2em+ffsC8MsvvwCkev/fffddAGbNmgXUXqW9/vrrA3D7\n7bcD4bORndq1a5dSYfvvvz8Qao0V+3zhhReA8Dnp/996661AqOs86aSTgNrbmaf1esMNNwBBCX/3\n3XcAqXiw1tYNN9yQsqnixg0aNADC5yLvT8p45MiRwH8rVqw1qS44eWZLLbUUAIMHDwbgm2++AcK6\nq1OnDgcffHCV3ymXeSlWxMYYE5modcR6skl9qTZSMUjFx6SwPv30UwC6d+8OhFpAqdeZM2cC0Lx5\n81RW9NdffwWCsmjTpg0QOpbU2y+Fq5+TmtH//+yzzwC45JJLAHj44YeBoOZi12PmW4up7Ps555wD\nBKXw7bffAtC+fXu9PlCp3qSAZZt9990XgEcffRSYX+Hqd+XxXHfddQApr+Xss88Gqo8ZJ822devW\nBeDJJ58EgrelNXTXXXcBcO655wJBIXft2pUDDzwQCPWt8vKWXHJJIHhkUsTKl2juh1RgtsS2LWRv\n38UXXxwI372uXbsCIX+hemHNkZCNtD/MmTMn9RoDBgwA4LfffgNgk002AUIHrtT1bbfdBsDkyZOB\n3PMariM2xpiEEFURK16jzOUFF1wAZD+DVZlj1bHqz5kzZ6ZmSmy66aZAeHrqCfjjjz8CQZWpY+nn\nn38GQreY6jnVqde4cWMgdOpJccdWFtWpCt3vvKoAwnwIeR2KV6oyJV15SeV+/vnnrLzyykD4nJRx\nzuIeAbjpppsA2HPPPQFYffXVgerVXLnadgE/B8AXX3wBBJvuuOOOAIwbNw4InqA8PeVCZs6cmVJ1\n559/PgAvvvgiED4PqT3VICtOqsoArffvv/8+q/cW27aQ2b6y66uvvgqEiofHH38cIFXXrpprrXHl\nlvbZZx+gcu6MKnr0M9r/FCuWnfUZad3fe++9AJx88slVfj4TVsTGGJMQoihiPdWVLdZpBFKtud6T\nnpi9evUC4KKLLkqpkv322w8I9ZaaPZFrtlT3pkoN1Sxrwn9sZVFd/P3MM88EgvqSgh8xYgQQYo96\nX5rLqmy8eu5V3zp69OhU/FjeQ660aNECCLkAKWRVUaRTbratDsV8TznlFKBymhqEOG51yB5du3ZN\nxdlVcZKJTp06AaESRV7FRhttBGT+LsW2LWS2r3ISr732GhDqrjVJLVsWWWSR1FQ1eYhSwNpDVGus\nGLGmNyoPotrvHXbYAchc8WNFbIwxCSGKIlZm+JZbbgFCZviDDz6o6esDcPXVV6eeloWqr1S2VU9S\nxYzPOOMMIL6ySFcVqoZ45513gFAHfMwxxwDw/vvvA/DVV18Bpa1DlVqXupD6UOw5fU2Wm23T0dqQ\n1yXvY9dddy3ynQVUfyy12LZtWyBUv1RHbNtCZvsOHz4cCPMhpJBLuXd169YNCHFp7S+yd3VYERtj\nTEKI0lmnmJjiMapgqCl6Qup0g0IixajYst5DudKqVSsgqEzFhJ955plo9ySUsVY9reLYilcr+50U\nlC+Q16EqoFKi+KmqXuRlZlLE5Yxq+lUJokqSGDNKnn/+eQDeeustAC6++GIA7r77biD0MOSLFbEx\nxkQmiiJWRYOebElQQOl1h1Ie5YpqtHW/qigpJ6TOVW2gjLbOE0sKqgPWGtGciFKSXnOvKpckozi3\nOhVV0RMTdTBOmjQJCBUy5513Xo1eN8pGrKSa3PwkDgyXG1quaBGrPEdJxnJCiUJtItrQkrYRpw++\nl81LiZKzIsY9FBqNwVWJmBLPMdGaHT16NAAHHXQQEEYm5DvAyqEJY4yJTBRFrHIlJcCSMCA8fQjQ\n1KlTY95ORnQ8uBo4ynFsZ/qR8CpPShoaFBNznKeapLRO8222KSfUXq/mFq3lmGiv0nhcDSDSQQoa\n4JQrVsTGGBOZKIo4vXlAia9yHhCuVl89pdWMUK4oZlgOKiITavCQik8aKr/U+9BgGcUTS4FyAiLX\ncZjlTLo3Wg4o56Jc0RZbbAHAAw88kNfrWREbY0xkoihiPdmkgNMzvuWIWpml5p977rmYt5MRxS1X\nWWWVyHeSGSkejShNGq+//joQqiZ01JcG3pcCDd1XY0G55zCyQfZUXFbeaE2bJwpBenmgvKF8sSI2\nxpjIRJGiGnspddmuXTugPOOuGmWoo9CVJS1UW3ax0OhFDV2X6iynChUV6itHkG/GOTY6RGDYsGEA\n7LHHHkAY3l7MOL1st+222wLhOKYk1uanM2HCBCCoTQ3e0XcwJr179wbCWF0dLJEvVsTGGBOZKIp4\n7NixQKgj1XHXOvalHKon1NXz1FNPAeFeFf8rpwzuglBWV0fZa1RjtgPHS8FKK61U5e/Tpk2LdCc1\nQ16GRozqSJ8xY8YA4YDaYqjUI444AgjxUw0wrw1oLK5qojUcSsPzY3wHdcSYDjDWgKeadjJaERtj\nTGSiHh7ap08fIAxZ1/jKG2+8Ecg9nqmYY/369XPuLFLlhobjXHrppUB4Gm+wwQYAzJgxY4G/H3vA\ndrptdVSOjkDq0qULUF5xeB06qgMZW7duDczfFVZuts3EcsstB4QYp0ZR6sh2xRVrgrL2Uo2aw7D1\n1lsD2X93YtsWMtu3b9++QDi2aPvttwcyH0FVSNLtLQWsMbPVeTseDG+MMQkhqiJWxlfDlhXP1NHV\nmmikrLTuVcpX1RYaGC0VWKdOndQgZx1ImX68uAa7H3vssQBss802Ve5BynGnnXYCMh8lFFtZpNtW\nMWFl0TV6dLPNNgNKezRSOrK9Do/Vn+pOSqfcbJstOtJHdcaKzyvOqM8mF3SYwqhRo4CQy1httdUA\nmD59ek6vF9u2kNm++r6/+OKLQOhy7dixI1DcqhTF3nX8ley81lprAZnzGlbExhiTEKIqYqH4rIYr\nH3300UCYV6ynvOIzmlurPm91ZCkWV79+fZo3bw4EBZHe+ZKecb3vvvsAGDx4MBA6k5ISa6vOtoqn\n3X777UAYaL3bbrsBpR3Kv8QSSwDwyiuvAGG2hKplPv744wX+XrnaNlv0GTzyyCNAeJ+qwJH39ddf\nf1W73rR+Bw0aBMBRRx0FhEoNHTuVK7FtC9nbVzM85EHLq1NcvLr1kw9aq6rQ6Ny5MxCqVB588MGs\nXseK2BhjEkJZKOJ5/h0gpWalGKTePv/8cwDGjRsHhKOt0zv15s6dm3qi6bXWWGMNIGQ39VqKndZU\nGcZWFtXZVkpKx63L65CtNS3qggsuAIoz70Gx/Pvvvx+ANm3aANC/f38g1JFXR7naNld0fJWqhDQh\nTdUVkydPTnlm8gLl9enz2XvvvYHgucmG+X6PY9sWcrevPKj0bjZVjhxyyCFAfhPoFHO/5557gFDJ\nIzvfcsstQOE9ZStiY4yJTFkp4iSRPrshtrLI1rbLLLMMEGJcin2pc1BxeP27uiB/+OEHoLJ+UipN\n1Q86nUAxPNVaywtRHajUuf6ebVwzKbbNhLL/imXKbuKff/5JTRZLz23od5W979GjR+p3akJs20L+\n9tU61AGeqpCSZzxixAgg9ARMmTJlvtnnqmDR7AjFm1UnrGoqfS+yeC9A7vuCFbExxkTGirhAxFYW\nudpWSksKWafRqrNQsTGpDj3pF0R6BYpUnTrkFLvTOV8vvfQSkPyKlHzRzATZXHWww4YNS53nqFiw\nJtIpW6/8SKG+t7FtC4Wzb8OGDYGwho8//nggVOdUVFSk1qr+lB0Vk7/88ssBuOmmm4D5z1XMhBWx\nMcYkFCviDOgJJwWZ/iQVsZVFoWyr96uqE3WHKZ7ZsGHDVFxSivejjz4CgnrQn+okq+k0vdpi23le\nDwhxYK2leT2LTOutUMS2LRRvX0ifY9yqVavU2pQnqH4Bde0VekKeFbExxiQEK+IMpMdGq7NXbGWR\nRNtmi21bPGLbFmxfsCI2xpjolFQRG2OMmR8rYmOMiYw3YmOMiYw3YmOMiYw3YmOMiYw3YmOMiYw3\nYmOMiYw3YmOMiYw3YmOMiYw3YmOMiYw3YmOMiYw3YmOMiYw3YmOMiYw3YmOMiYw3YmOMiYw3YmOM\niYw3YmOMiYw3YmOMiYw3YmOMiYw3YmOMiYw3YmOMiYw3YmOMiYw3YmOMiYw3YmOMicz/A4e8nPwN\nooqLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0c41527b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1899\n",
      "1899: [D loss: 0.384078, acc: 0.834123]  [A loss: 2.497276, acc: 0.130850]\n"
     ]
    },
    {
     "data": {
      "image/png": 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PyRdJs63Ukvzu7777LhDsESMCR0p41KhRAPTr1w+oODomtm0h96bC2sWqJk27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\nwRsRO2e/mM1DK0IZk8qA2n777YHKZyM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0ec661fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1999\n",
      "1999: [D loss: 0.380695, acc: 0.835586]  [A loss: 2.522729, acc: 0.129014]\n"
     ]
    },
    {
     "data": {
      "image/png": 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0qR1qqY1cTkXYcccdgaA+pGS04qmNUo+txpoUmVZV6sLq27cvkF1NeuvWrQEY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\nPBEbY0xk/h9Z/zNbhI673wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0bc761748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_metrics_complete, d_metrics_complete = train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd0ec359940>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NNnoqCbSqX53v/3Gy91wFhUV0T6nD0nSbdSh+gwX/PgTTpmFN2nhcI86Nprj7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0bc5f5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.DataFrame(\n",
    "    {\n",
    "        'Adversarial': [metric[0] for metric in a_metrics_complete],\n",
    "        'Discriminator': [metric[0] for metric in d_metrics_complete],\n",
    "    }\n",
    ").plot(title='Training Loss', logy=True)\n",
    "ax.set_xlabel(\"Epochs\")\n",
    "ax.set_ylabel(\"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd0c421e048>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j7Rna3xq14LbZMPg9GxnqIw6bzZGTZ+NGh2DoHLgrRlC0k/8ZfbW55bsqg8fv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\n/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0c42de278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.DataFrame(\n",
    "    {\n",
    "        'Adversarial': [metric[1] for metric in a_metrics_complete],\n",
    "        'Discriminator': [metric[1] for metric in d_metrics_complete],\n",
    "    }\n",
    ").plot(title='Training Accuracy')\n",
    "ax.set_xlabel(\"Epochs\")\n",
    "ax.set_ylabel(\"Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
